Development of high quantum efficiency, flat panel, thick detectors for megavoltage x‐ray imaging: A novel direct‐conversion design and its feasibility
Bibliographic record
Abstract
Most electronic portal imaging devices (EPIDs) developed to date, including recently developed flat panel systems, have low x-ray absorption, i.e., low quantum efficiency (QE) of 2%-4% as compared to the theoretical limit of 100%. A significant increase of QE is desirable for applications such as a megavoltage cone-beam computed tomography (MVCT) and megavoltage fluoroscopy. However, the spatial resolution of an imaging system usually decreases significantly with an increase of QE. The key to the success in the design of a high QE detector is therefore to maintain the spatial resolution. Recently, we demonstrated theoretically that it is possible to design a portal imaging detector with both high QE and high resolution [see Pang and Rowlands, Med. Phys. 29, 2274 (2002)]. In this paper, we introduce such a novel design consisting of a large number of microstructured plates (made by, e.g., photolithographic patterning of evaporated or electroplated layers) packed together and aligned with the incident x rays. On each plate, microstrip charge collectors are focused toward the x-ray source to collect charges generated in the ionization medium (e.g., air or gas) surrounded by high-density materials that act as x-ray converters. The collected charges represent the x-ray image and can be read out by various means, including a two-dimensional (2-D) active readout matrix. The QE, spatial resolution, and sensitivity of the detector have been calculated. It has been shown that the new design will have a QE of more than an order of magnitude higher and a spatial resolution equivalent to that of flat panel systems currently used for portal imaging. The new design is also quantum noise limited down to very low doses (approximately 1-2 radiation pulses of the linear accelerator).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".